Quantitative fisheries stock assessment : , Quantitative fisheries stock assessment : , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Quantitative fisheries stock assessment

The literature describing methods for estimating animal abundance and related parameters continues to grow. This paper reviews recent developments in the subject over the past seven years and updates two previous reviews.

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Estimating Animal Abundance: Review III

For the vast majority of cases, it is highly unlikely that all the individuals of a population will be encountered during a study. Furthermore, it is unlikely that a constant fraction of the population is encountered over times, locations, or species to be compared. Hence, simple counts usually will not be good indices of population size. We recommend that detection probabilities (the probability of including an individual in a count) be estimated and incorporated into inference procedures. However, most techniques for estimating detection probability require moderate sample sizes, which may not be achievable when studying rare species. In order to improve the reliability of inferences from studies of rare species, we suggest two general approaches that researchers may wish to consider that incorporate the concept of imperfect detectability: (1) borrowing information about detectability or the other quantities of interest from other times, places, or species; and (2) using state variables other than abundance (e.g., species richness and occupancy). We illustrate these suggestions with examples and discuss the relative benefits and drawbacks of each approach.

Improving inferences in population studies of rare species that are detected imperfectly

Estimating Animal Abundance

The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher’s knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

A length-structured population model, which incorporates von Bertalanffy growth, is used to describe changes in population abundance over time. The model is incorporated into a catch-at-length algo...

A Catch-at-Length Analysis that Incorporates a Stochastic Model of Growth

Overview and Background Size-Based Methods of Stock Assessment for Applications to Tropical Artisanal Fisheries Coral Reef Fishery Sampling Methods Sampling Methods for Assessment of Tropical Fisheries in Developing Countries The Application of Some Acoustic Methods for the Assessment of Tropical Artisanal Fisheries Age Determination in Biology: A Perspective on Methods and Applications to Stock Assessment The Application of Time Series Analysis to Fisheries Population Assessment and Modeling Empirical Methods and Models for Multispecies Stock Assessment A System Science Approach to Fisheries Stock Assessment and Management References Annexes Index

Han-Lin Lai

Papers

A Catch-at-Length Analysis that Incorporates a Stochastic Model of Growth

Stock Assessment: Quantitative Methods and Applications for Small Scale Fisheries

Effects of parameter variability on length-cohort analysis